The approach followed in the course: Computational Embodied Neuroscience (CEN).The
goal of CEN (understanding the overall brain organisation underlying
behaviour, and the type of computational models used to this purpose).The 8 fundamental methodological principles of CEN.

Case study on an integrative model on intrinsic motivations, extrinsic motivations, and the hierarchical organisation of actions in brain. The model encompasses:- superior colliculus and learning of actions based on intrinsic motivations;- the amygdala-based system for extrinsic motivations for goal-directed behaviour (the amygdala-based system n. 6/6, see below);- the hierarchical organisation of actions in cortex and basal ganglia;

Case study: a model of stress coping and broad neuromodulation of brain (related to amygdala-based system n. 2/6). A few details about the simulation of the effects of the neuromodulators on the synaptic transmission.

Guide to the preparation of the exam in relation to the short thesis and the oral test based on the lectures' material

This section indicates how to prepare the exam. It distinguishes
between the students that attended the lectures and those that did not.

Students that have attended the lectures and labs

The preparation of the students that attended the course has to achieve this goals:

Carry out a small research, based on a computational model
programmed by the student, on a topic chosen from those considered
during the course. This should lead also to compile a short thesis
(''tesina''). The short thesis has to be maximum 12 pages long (use
character 12) including figures, references and all the rest. The first
page should report: Author(s), title, abstract (max 250 words;
indicating topic, problem, type, of model, and results), ''Tesina per il
corso Neuroscienze Computazionali, Anno accademico 20xx-20xx'', Prof.
Gianluca Baldassarre, tutors (if present). The short thesis can be
either in Italian or in English. The short thesis has to besent to Gianluca at least 1 week before the examvia email.

The
students have also to sustain an oral exam. This will be on the
''tesina'' and on the issues explained during the theoretical part of
the course. The student should prepare this part of the exam on the
basis of the slides, suitably integrated with your notes and the info drawn from the papers available below, and on the basis of the sections of the handbook Anastasio indicated below, and on the basis of 1 freely-selected paper among the papers listed below.

This is a complete list of parts you should study from the Anastasio handbook:
Chapters: 1, 2, 3, 4, 5, 6, 11.

You can also find complementary information on these issues in the book of Trappenberg:

Chapter 1: Introduction

Paragraph 2.1: Biological background

Paragraph 2.2: Basic synaptic mechanisms and dendritic processing

Paragraph 3.1.1: The leaky integrate-and-fire neuron

Paragraph 4.1: Associative memory and Hebbian learning

Paragraph 4.3: Mathematical formulation of Hebbian plasticity

Paragraph 7.1: Competitive feature representations in cortical tissue

Paragraph 7.2: Self-organising maps

Paragraph 9.6: Reinforcement learning

Students that did not attend the lectures and labs

The preparation of the students that could not attend the course have
to be as for the students who attended. As they could not take notes,
they should integrate the slides with the information from the papers
listed above, from internet, and from other parts of the book Anastasio
not listed above. The students should study Matlab based on the material
indicated above. Also the students who did not attend the course should send their thesis to Gianluca at least 1 week before the examvia email.